Diagnosing method of golf swing

ABSTRACT

A camera  10  photographs a golf player swinging a golf club to hit a golf ball and the golf club. Image data is obtained by photographing. A calculating part  16  extracts a plurality of frames from the image data. The calculating part  16  determines a check frame in which the golf player is in a predetermined posture from the plurality of frames. The calculating part  16  determines a contour of the golf player from the check frame. The calculating part  16  decides a swing from the contour of the golf player. An extreme value constituting the contour is determined in deciding the swing. A feature point is determined from the extreme value. The swing is diagnosed using the feature point.

The present application claims priority on Patent Application No.2011-290298 filed in JAPAN on Dec. 29, 2011, the entire contents ofwhich are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a diagnosing method of quality of agolf swing.

2. Description of the Related Art

When a golf player hits a golf ball, the golf player addresses so that aline connecting right and left tiptoes is approximately parallel to ahitting direction. In a right-handed golf player's address, a left footis located on a front side in the hitting direction, and a right foot islocated on a back side in the hitting direction. In the address, a headof a golf club is located near the golf ball. The golf player starts atakeback from this state, and raises up the head backward and thenupward. A position where the head is fully raised up is a top. Adownswing is started from the top. A start point of the downswing isreferred to as a quick turn. The head is swung down after the quickturn, and the head collides with the golf ball (impact). After theimpact, the golf player swings through the golf club forward and thenupward (follow-through), and reaches a finish.

In improvement in skill of a golf player, it is important to acquire asuitable swing form. Swing diagnosis is conducted so as to contribute tothe improvement in the skill. In the swing diagnosis, a swing isphotographed by a video camera. The swing may be photographed in orderto collect materials useful for development of golf equipment.

In classic swing diagnosis, a teaching pro or the like views a movingimage and points out problems during a swing. On the other hand, anattempt to diagnose the swing using image processing is also conducted.In the image processing, a frame required for diagnosis needs to beextracted from a large number of frames. It is necessary to extract asilhouette of a photographic subject from these frames. An example of amethod for extracting the silhouette of the photographic subject isdisclosed in Japanese Patent Application Laid-Open Nos. 2005-210666(US2005/0143183) and 2011-78069.

In the method disclosed in Japanese Patent Application Laid-Open No.2005-210666, the photographic subject and a background scene aredistinguished by using difference processing. The silhouette of thephotographic subject is extracted by the difference processing. In themethod disclosed in Japanese Patent Application Laid-Open No.2011-78069, the photographic subject and the background scene aredistinguished by using a silhouette extracting method. In the silhouetteextracting method, the silhouette of the photographic subject isextracted by using a luminance histogram and a color histogram of apixel constituting the frame.

For example, a predetermined silhouette such as a silhouette of anaddress is specified from the plurality of extracted silhouettes. Thequality of the swing is decided from the specified silhouette. Qualityjudgement can be automated by extracting suitable information from thesilhouette. When the extracted information is suitable, the quality ofthe swing can be accurately decided.

It is an object of the present invention to provide a method capable ofreadily and accurately diagnosing quality of a swing.

SUMMARY OF THE INVENTION

A diagnosing method of a golf swing according to the present inventionincludes the steps of:

a camera photographing a golf player swinging a golf club to hit a golfball and the golf club to obtain image data;

obtaining a plurality of frames from the image data and determining acheck frame in which the golf player is in a predetermined posture fromthe plurality of frames;

determining a contour of the golf player from the check frame; and

deciding the swing from the contour of the golf player.

An extreme value constituting the contour is determined in the step ofdeciding the swing; a feature point is determined from the extremevalue; and the swing is diagnosed using the feature point.

Preferably, in the diagnosing method, the extreme value constitutes acontour of a head part, a contour of a waist, or a contour of a heel.

Preferably, in the diagnosing method, two or more extreme values orreference points obtained from the extreme values are determined. Apoint on the contour is the feature point, wherein a distance between astraight line passing through the two reference points and the point onthe contour is maximized or minimized.

Preferably, in the diagnosing method, two or more extreme values orreference points obtained from the extreme values are determined. Apoint on the contour is determined to be a control point of a Beziercurve, wherein a distance between a straight line passing through thetwo reference points and the point on the contour is maximized orminimized. The contour is approximated with the Bezier curve. Stillanother feature point is determined based on the Bezier curve when thecontour is most approximated.

Preferably, in the diagnosing method, the point on the contour isdetermined as a reference point based on the extreme value; the contourincluding the reference point is subjected to polynomial approximationto obtain an approximate line; and a point on the approximate line asthe extreme value is still another feature point.

Preferably, in the diagnosing method, a part of the contour of which arelative position from the feature point is specified as a template. Thetemplate is matched with another region of the contour. When thetemplate is most approximated to another region of the contour, a pointof a position corresponding to the feature point specified from thetemplate is still another feature point.

Preferably, in the diagnosing method, a point on a straight lineextended from the another feature point and having a maximum edge isstill another feature point.

Preferably, in the diagnosing method, a point determined based on ageometrical position relation of a region of a human body from theextreme value, a reference point obtained from the extreme value, or thefeature point is still another feature point.

Preferably, in the diagnosing method, the geometrical position relationof the region of the human body is a position relation in the checkframe in which the golf player is in the predetermined posture.

Preferably, in the diagnosing method, a predetermined search area on thebasis of the extreme value, a reference point obtained from the extremevalue, or the feature point is set. A point which is the extreme valuein the search area is still another feature point.

Preferably, in the diagnosing method, the predetermined search area isset based on a geometrical position relation between the extreme value,the reference point, or the feature point and the region of the humanbody.

Preferably, the geometrical position relation of the region of the humanbody is a position relation in the check frame in which the golf playeris in the predetermined posture.

Preferably, a binary image of a silhouette of the golf player isobtained from the check frame in the step of determining the contour ofthe golf player from the check frame. The contour of the golf player isdetermined from the binary image.

Preferably, a difference image is obtained by subjecting the pluralityof frames to difference processing in the step of determining thecontour of the golf player from the check frame. The contour of the golfplayer is determined from the difference image.

Preferably, the diagnosing method further includes the step ofconducting camera shake correction, and the plurality of frames obtainedfrom the image data are subjected to the camera shake correction.

Preferably, the image data is subjected to the camera shake correctionin the step of conducting the camera shake correction.

A diagnosing system of a golf swing according to the present inventionincludes:

(A) a camera photographing a golf player swinging a golf club to hit agolf ball and the golf club;

(B) a memory storing photographed image data; and

(C) a calculating part. The calculating part includes:

(C1) a function for extract a plurality of frames from the image data;

(C2) a function for determining a check frame in which the golf playeris in a predetermined posture from the plurality of frames;

(C3) a function for determining a contour of the golf player of thecheck frame;

(C4) a function for determining an extreme value from the contour;

(C5) a function for determining a feature point from the extreme value;and

(C6) a function for diagnosing the swing using position information ofthe feature point.

Preferably, the calculating part of the diagnosing system has a functionfor subjecting the image data to camera shake correction.

In the method according to the present invention, the extreme value isdetermined from the contour. The feature point is determined from theextreme value. The quality of the golf swing is diagnosed using theposition information of the feature point. The quality of the golf swingcan be readily and accurately diagnosed by using the extreme value andthe feature point.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual view showing a swing diagnosing system accordingto one embodiment of the present invention;

FIG. 2 is a flow chart showing a diagnosing method of a golf swingconducted by the system of FIG. 1;

FIG. 3 is an illustration showing a screen of a camera of FIG. 1;

FIG. 4 is a flow chart showing a determining method of a check frame;

FIG. 5 is a flow chart showing a method determining a frame of anaddress;

FIG. 6 is an illustration for a Sobel method;

FIG. 7 is a binarized image;

FIG. 8 is a flow chart showing a method determining a frame of animpact;

FIG. 9A is an image showing a result of a difference between a 44thframe and a reference frame; FIG. 9B is an image showing a result of adifference between a 62th frame and the reference frame; FIG. 9C is animage showing a result of a difference between a 75th frame and thereference frame; FIG. 9D is an image showing a result of a differencebetween a 76th frame and the reference frame; FIG. 9E is an imageshowing a result of a difference between a 77th frame and the referenceframe; FIG. 9F is an image showing a result of a difference between a78th frame and the reference frame;

FIG. 10 is a graph showing a difference value;

FIG. 11 is a flow chart showing a method determining a frame of a top;

FIG. 12 is a graph showing a difference value;

FIG. 13 is a flow chart showing a method determining a frame of apredetermined position of a takeback;

FIG. 14A is an image showing a result of a difference between a 30thframe and the reference frame; FIG. 14B is an image showing a result ofa difference between a 39th frame and the reference frame; FIG. 14C isan image showing a result of a difference between a 41th frame and thereference frame; FIG. 14D is an image showing a result of a differencebetween a 43th frame and the reference frame; FIG. 14E is an imageshowing a result of a difference between a 52th frame and the referenceframe; FIG. 14F is an image showing a result of a difference between a57th frame and the reference frame;

FIG. 15 is a flow chart showing a method in which a contour isdetermined by silhouette extraction;

FIG. 16 is an illustration for showing a mask for the silhouetteextraction of FIG. 15;

FIG. 17 is a flow chart showing the details of some steps of thesilhouette extraction of FIG. 15;

FIG. 18 is a luminance histogram of a certain pixel;

FIG. 19 is a luminance histogram of another pixel;

FIG. 20 is a luminance histogram of still another pixel;

FIG. 21 is a color histogram of a pixel of FIG. 18;

FIG. 22 is a color histogram of a pixel of FIG. 19;

FIG. 23 is a color histogram of a pixel of FIG. 20;

FIG. 24 is a flowchart showing a first stage of a deciding step of themethod of FIG. 15;

FIG. 25 is a flow chart showing a second stage of the deciding step ofthe method of FIG. 15;

FIG. 26 is a flow chart showing a third stage of the deciding step ofthe method of FIG. 15;

FIG. 27 is an illustration for showing a silhouette obtained in themethod of FIG. 15;

FIG. 28 is an illustration for showing the contour of the silhouette ofFIG. 27;

FIG. 29 is a flowchart of a deciding method using a feature point;

FIG. 30 is an illustration for determining a feature point from acontour of an address;

FIG. 31 is an illustration for determining another feature point fromthe contour of the address;

FIG. 32 is an illustration for determining still another feature pointfrom the contour of the address;

FIG. 33 is an illustration for determining still another feature pointfrom the contour of the address;

FIG. 34 is an illustration for determining still another feature pointfrom the contour of the address;

FIG. 35 is an illustration for determining still another feature pointfrom the contour of the address;

FIG. 36 is an illustration for determining still another feature pointfrom the contour of the address;

FIG. 37 is an illustration for determining a feature point from acontour of a predetermined position during a takeback;

FIG. 38 is an illustration for determining another feature point fromthe contour of the predetermined position during the takeback;

FIG. 39 is an illustration for determining still another feature pointfrom the contour of the predetermined position during the takeback;

FIG. 40 is an illustration for determining a feature point from acontour of a top;

FIG. 41 is an illustration for determining another feature point fromthe contour of the top;

FIG. 42 is an illustration for determining still another feature pointfrom the contour of the top;

FIG. 43 is an illustration for determining a feature point from acontour of an impact;

FIG. 44 is an illustration for determining another feature point fromthe contour of the impact;

FIG. 45 is an illustration for deciding a swing from a contour of anaddress; and

FIG. 46 is an illustration for determining a feature point from an imagesubjected to difference processing.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

Hereinafter, the present invention will be described below in detailbased on preferred embodiments with reference to the drawings.

A diagnosing system 2 shown in FIG. 1 is provided with a mobiletelephone 4 and a server 6. The mobile telephone 4 and the server 6 areconnected each other via a communication line 8. The mobile telephone 4is provided with a camera 10, a memory 12, and a transmitting/receivingpart 14. Specific examples of the memory 12 include a RAM, an SD card(including a mini SD and a micro SD or the like), and other memorymedium. The server 6 is provided with a calculating part 16, a memory18, and a transmitting/receiving part 20. The calculating part 16 istypically a CPU.

A flow chart of diagnosing method of a golf swing conducted by thesystem 2 of FIG. 1 is shown in FIG. 2. In the diagnosing method,photographing is conducted by the camera 10 (STEP1). A screen beforephotographing is started is shown in FIG. 3. The screen is displayed ona monitor (not shown) of the mobile telephone 4. An address of a golfplayer 24 having a golf club 22 is photographed on the screen. On thescreen, the golf player 24 is photographed from a back side. A firstframe 26 and a second frame 28 are shown on the screen. These frames 26and 28 are displayed by software executed on a CPU (not shown) of themobile telephone 4. The frames 26 and 28 contribute to a case where aphotographer determines an angle of the camera 10. The photographerdetermines an angle of the camera 10 so that the first frame 26 includesa grip 30 and the second frame 28 includes a head 32. Furthermore, theframes 26 and 28 contribute to determination of a distance between thecamera 10 and the golf player 24.

Photographing is started from the state shown in FIG. 3. After thephotographing is started, the golf player 24 starts a swing. Thephotographing is continued until a golf ball (not shown) is hit and theswing is ended. Moving image data is obtained by the photographing. Thedata includes a large number of frames. These frames are stored in thememory 12 (STEP2). The number of pixels of each of the frames is, forexample, 640×480. Each of the pixels has RGB system color information.

The photographer or the golf player 24 operates the mobile telephone 4to transmit the moving image data to the server 6 (STEP3). The data istransmitted to the transmitting/receiving part 20 of the server 6 fromthe transmitting/receiving part 14 of the mobile telephone 4. Thetransmission is conducted via the communication line 8. The data isstored in the memory 18 of the server 6 (STEP4).

The calculating part 16 conducts camera shake correction (STEP5). Asdescribed later, the diagnosing method according to the presentinvention conducts difference processing between the frames. The camerashake correction enhances the accuracy in the difference processing. Afeature point is diverted between the frames. The camera shakecorrection increases the accuracy of the position of the feature point.An example of a method for the camera shake correction is disclosed inJapanese Patent Application No. 2011-78066. When the mobile telephone 4has a sufficient camera shake correction function, the camera shakecorrection conducted by the calculating part 16 can be omitted.

The calculating part 16 determines a frame presented in order to decidequality of a swing from a large number of frames (STEP6). Hereinafter,the frame is referred to as a check frame. For example, framescorresponding to the following items (1) to (6) are extracted:

(1) an address

(2) a predetermined position during a takeback

(3) a top

(4) a quick turn

(5) an impact

(6) a finish

The predetermined position during the takeback includes a position wherean arm is horizontal. The quick turn implies a state immediately afterstart of the downswing. In the quick turn, the arm is substantiallyhorizontal. The details of an extracting step (STEP6) of the check framewill be described later.

The calculating part 16 determines a contour of a photographic subjectin each of the check frames (STEP7). Specifically, the calculating part16 determines a contour of a body of the golf player 24 or the contourof the body of the golf player 24 and a contour of the golf club 22. Thecalculating part 16 decides the quality of the swing based on thecontour (STEP8).

The deciding result is transmitted to the transmitting/receiving part 14of the mobile telephone 4 from the transmitting/receiving part 20 of theserver 6 (STEP9). The deciding result is displayed on the monitor of themobile telephone 4 (STEP10). The golf player 24 viewing the monitor canknow a portion of the swing which should be corrected. The system 2 cancontribute to improvement in skill of the golf player 24.

As described above, the calculating part 16 determines the check frame(STEP6). The calculating part 16 has the following functions:

(1) a function for obtaining an edge image of a frame extracted from theimage data;

(2) a function for subjecting the edge image to binarization based on apredetermined threshold value to obtain a binary image;

(3) a function for subjecting the binary image to Hough transformprocessing to extract a position of a shaft of the golf club 22, andspecifying a tip coordinate of the golf club 22;

(4) a function for contrasting tip coordinates of different frames todetermine a temporary flame in the address;

(5) a function for calculating color information in the reference areaof each of frames by backward sending from a frame after the temporaryframe by a predetermined number, and determining a frame in the addressbased on change of the color information;

(6) a function for using a frame after the frame in the address by apredetermined number as a reference frame, calculating a differencevalue between each of frames after the reference frame and the referenceframe, and determining a frame of an impact based on change of thedifference value;

(7) a function for calculating a difference value between each of aplurality of frames before the frame of the impact and a previous framethereof, and determining a frame of a top based on the difference value;

(8) a function for calculating a difference value between each of aplurality of frames after the frame of the address and the frame of theaddress;

(9) a function for subjecting the difference value of each of the framesto Hough transform processing to extract the position of the shaft; and

(10) a function for determining a frame of a predetermined positionduring a takeback based on the change of the position of the shaft.

A flow chart of a determining method of the check frame is shown in FIG.4. The determining method includes a step of determining the frame ofthe address (STEP61), a step of determining the frame of the impact(STEP62), a step of determining the frame of the top (STEP63), a step ofdetermining the frame of the predetermined position of the takeback(STEP64), and a step of determining the frame of the finish (STEP65).The predetermined position of the takeback is, for example, a positionwhere the arm is horizontal. The step of determining the frame of thefinish (STEP65) may be omitted.

The step of determining the frame of the finish (STEP65) can determine aframe after the frame of the impact by a predetermined number as theframe of the finish, for example. The step of determining the frame ofthe finish (STEP65) may be the same as the step of determining the frameof the top (STEP63).

Other check frame may be determined based on the frame determined by themethod shown in FIG. 4. For example, a frame before the frame of theimpact by a predetermined number can be defined as a frame of a quickturn.

A flow chart of a method for determining the frame of the address isshown in FIG. 5. In the method, each of the frames is converted into agrayscale image from an RGB image (STEP611). The conversion is conductedin order to facilitate subsequent edge detection. A value V in thegrayscale image is calculated by, for example, the following numericalexpression.V=0.30·R+0.59·G+0.11·B

The edge is detected from the grayscale image and the edge image isobtained (STEP612). In the edge, change of a value V is great.Therefore, the edge can be detected by differentiating or takingdifferences of the change of the value V. A noise is preferably removedin the calculation of the differentiation or the difference. A Sobelmethod is exemplified as an example of the method for detecting theedge. The edge may be detected by other method. A Prewitt method isexemplified as the other method.

FIG. 6 is an illustration for the Sobel method. Characters A to I inFIG. 6 represent values V of the pixels. A value E′ is calculated from avalue E by the Sobel method. The value E′ is edge intensity. The valueE′ is obtained by the following numerical expression.E′=f _(x) ² +f _(y) ²)^(1/2)In the numerical expression, f_(x) and f_(y) are obtained by thefollowing numerical expression.f _(x) =C+2·F+I−(A+2·D+G)f _(y) =G+2·H+I−(A+2·B+C)

Each of the pixels of the edge image is binarized (STEP613). A thresholdvalue for binarization is suitably determined according to the weatherand the time or the like. A monochrome image is obtained by thebinarization. An example of the monochrome image is shown in FIG. 7.

Data of the monochrome image is presented for Hough transform (STEP614).The Hough transform is a method for extracting a line from an imageusing regularity of a geometric shape. A straight line, a circle, and anellipse or the like can be extracted by the Hough transform. In theinvention, a straight line corresponding to the shaft of the golf club22 is extracted by the Hough transform.

The straight line can be represented by an angle θ between a lineperpendicular to the straight line and an x-axis, and a distance ρbetween the straight line and a origin point. The angle θ is a clockwiseangle having a center on the origin point (0, 0). The origin point is onthe upper left. The straight line on an x-y plane corresponds to a pointon a θ-ρ plane. On the other hand, a point (x_(i), y_(i)) on the x-yplane is converted into a sine curve represented by the followingnumerical expression on the θ-ρ plane.ρ=x _(i)·cos θ+y _(i)·sin θWhen points which are on the same straight line on the x-y plane areconverted into the θ-ρ plane, all sine curves cross at one point. When apoint through which a large number of sine curves pass in the θ-ρ planebecomes clear, the straight line on the x-y plane corresponding to thepoint becomes clear.

Extraction of a straight line corresponding to the shaft is attempted bythe Hough transform. In a frame in which the shaft is horizontal in thetakeback, an axis direction of the shaft approximately coincides with anoptical axis of the camera 10. In the frame, the straight linecorresponding to the shaft cannot be extracted. In the embodiment, ρ isnot specified; θ is specified as 30 degrees or greater and 60 degrees orless; x is specified as 200 or greater and 480 or less; and y isspecified as 250 or greater and 530 or less. Thereby, the extraction ofthe straight line is attempted. Since θ is specified as the range, astraight line corresponding to an erected pole is not extracted. Astraight line corresponding to an object placed on the ground andextending in a horizontal direction is also not extracted. Falserecognition of a straight line which does not correspond to the shaft asthe straight line corresponding to the shaft is prevented by specifyingθ as 30 degrees or greater and 60 degrees or less. In the embodiment, instraight lines in which the number of votes (the number of pixelsthrough which one straight line passes) is equal to or greater than 150,a straight line having the greatest number of votes is regarded as thestraight line corresponding to the shaft. In the frame in which thestraight line corresponding to the shaft is extracted by the Houghtransform, the tip coordinate of the shaft (the tip position of thestraight line) is obtained (STEP615).

In the embodiment, the tip coordinate is obtained by backward sendingfrom a 50th frame after the photographing is started. A frame in whichthe moving distance of the tip between the frame and both the precedingand following frames is equal to or less than a predetermined value isdetermined as a temporary frame of the address (STEP616). In theembodiment, a f-th frame in which a tip is in the second frame 28 (seeFIG. 4) and the summation of the moving distances of (f−1)th to (f+2) thtips is equal to or less than 40 is defined as a temporary frame.

SAD (color information) of a plurality of frames before and after thetemporary frame is calculated (STEP617). SAD is calculated by thefollowing numerical expression (1).SAD=(RSAD+GSAD+BSAD)/3  (1)In the numerical expression (1), RSAD is calculated by the followingnumerical expression (2); GSAD is calculated by the following numericalexpression (3); and BSAD is calculated by the following numericalexpression (4).RSAD=(Rf1−Rf2)²  (2)GSAD=(Gf1−Gf2)²  (3)BSAD=(Bf1−Bf2)²  (4)

In the numerical expression (2), Rf1 represents an R value in the f-thsecond frame 28; Rf2 represents an R value in the (f+1)-th second frame28. In the numerical expression (3), Gf1 represents a G value in thef-th second frame 28; and Gf2 represents a G value in the (f+1)-thsecond frame 28. In the numerical expression (4), Bf1 represents a Bvalue in the f-th second frame 28; and Bf2 represents a B value in the(f+1)-th second frame 28.

SAD of each of the frames is calculated by backward sending from a frameafter the temporary frame by a predetermined number. In the embodiment,SAD of a frame after the temporary frame by 7 to a frame before thetemporary frame by 10 is calculated. A frame in which SAD is first lessthan 50 is determined as a true frame of the address (STEP618). Theframe is the check frame. When the frame in which SAD is less than 50does not exist, a frame in which SAD is the minimum is determined as thetrue frame of the address.

A flow chart of a method for determining the frame of the impact isshown in FIG. 8. Since the frame of the address has been alreadydetermined, the frame after the frame of the address by thepredetermined number is determined as a reference frame (STEP621). Thereference frame is a frame before the impact in which the golf club 22is not reflected in the second frame 28. In the embodiment, a frameafter the frame of the address by 25 is defined as the reference frame.

Difference processing is conducted between the reference frame and eachof the frames after the reference frame (STEP622). The differenceprocessing is processing known as one of image processings. Differenceimages are shown in FIGS. 9A to 9F. The details of the images are asfollows.

FIG. 9A: A difference image between a 44th frame and the reference frame

FIG. 9B: A difference image between a 62th frame and the reference frame

FIG. 9C: A difference image between a 75th frame and the reference frame

FIG. 9D: A difference image between a 76th frame and the reference frame

FIG. 9E: A difference image between a 77th frame and the reference frame

FIG. 9F: A difference image between a 78th frame and the reference frame

A difference value in the second frame 28 for the image after thedifference processing is calculated (STEP623). The difference value isshown in a graph of FIG. 10. The graph shows that the difference valueof the 77th frame is the largest. The 77th frame is determined as theframe of the impact (STEP624). The frame is the check frame.

A flow chart of a method for determining the frame of the top is shownin FIG. 11. The frame of the impact has been already determined.Difference processing of from the frame of the impact to a frame beforethe impact by a predetermined number is conducted (STEP631). Thedifference processing is conducted between the frame and a frame afterthe frame by 1. A difference value is obtained by the differenceprocessing. The difference value is shown in FIG. 12. In the embodiment,a frame in which a difference value is the minimum is selected between aframe before the impact by 15 and the frame of the impact (STEP632). Inthe example of FIG. 12, the 77th frame is the frame of the impact; and a65th frame is the frame of the top. The 65th frame is the check frame.

A flow chart of a method for determining the predetermined position ofthe takeback is shown in FIG. 13. The frame of the address has beenalready determined. The difference processing of frames after the frameof the address is conducted (STEP641). The frame of the address is usedas the reference frame, and the difference processing is conductedbetween the reference frame and other frame. Difference images are shownin FIGS. 14A to 14F. The details of the images are as follows.

FIG. 14A: A difference image between a 30th frame and the referenceframe

FIG. 14B: A difference image between a 39th frame and the referenceframe

FIG. 14C: A difference image between a 41th frame and the referenceframe

FIG. 14D: A difference image between a 43th frame and the referenceframe

FIG. 14E: A difference image between a 52th frame and the referenceframe

FIG. 14F: A difference image between a 57th frame and the referenceframe

In these difference images, the number of pixels of a longitudinal y is640, and the number of pixels of a transversal x is 480. Thesedifference images are subjected to Hough transform (STEP642). A straightline corresponding to the shaft can be calculated by the Houghtransform. In each of difference screens, the existence or nonexistenceof the straight line satisfying the following conditions is decided(STEP643).

θ: 5 degrees or greater and 85 degrees or less

ρ: no specification

x: 0 or greater and 240 or less

y: 0 or greater and 320 or less

number of votes: equal to or greater than 100

In the frame from which the straight line satisfying these conditions isextracted, the shaft is located on a left side than a waist of the golfplayer 24. A frame (hereinafter, referred to as a “matching frame”)after the frame of the address, from which the straight line satisfyingthese conditions is extracted first, is the check frame. A frame afterthe matching frame by a predetermined number may be determined as thecheck frame. In a frame after the matching frame by 2, it has been clearexperientially that a left arm of the right-handed golf player 24 isalmost horizontal.

The calculating part 16 determines the contour from the check frame(STEP7). A flow chart for determining the contour from the check frameis shown in FIG. 15. The calculating part 16 produces a whole frame setincluding all the frames for each of the pixels (STEP71). Thecalculating part 16 determines whether each of the pixels of each of theframes has an achromatic color or a chromatic color, and produces achromatic color frame set and an achromatic color frame set for each ofthe pixels (STEP72).

The calculating part 16 produces a luminance histogram (a firsthistogram) for the whole frame set (STEP73). In the luminance histogram,a frequency is a frame number and a class is luminance (first colorinformation). The luminance histogram may be produced based on anothercolor information. The calculating part 16 produces a color histogram (asecond histogram) for the chromatic color frame set and the achromaticcolor frame set (STEP74). In the color histogram, a frequency is a framenumber; a class for the chromatic color frame set is hue (second colorinformation); and a class for the achromatic color frame set isluminance (third color information). The class for the chromatic colorframe set may be color information other than hue. The class for theachromatic color frame set may be color information other thanluminance.

The calculating part 16 decides whether each of the frames of each ofthe pixels is a background scene or a photographic subject based on theluminance histogram and the color histogram (STEP75). Hereinafter,examples of main steps will be described in detail.

In the embodiment, a mask 36 shown in FIG. 16 is set in the first frame.As is apparent from FIG. 16, the mask 36 includes the golf player 24 andthe golf club 22 shown in FIG. 3. An outer edge of the mask 36 isoutside an outer edge of the golf player 24, and is outside an outeredge of the golf club 22. In determining whether each of the pixels hasan achromatic color or a chromatic color, a pixel included in the mask36 is not the object of calculation.

In a flow chart of FIG. 17, the details of a step (STEP72) ofdetermining whether each of the pixels has an achromatic color or achromatic color, and producing an achromatic color frame set and achromatic color frame set for each of the pixels are shown.

In the method, a chroma value sf of a pixel is calculated (STEP721). Forexample, when a silhouette is extracted based on sixty frames of thefirst frame to the 60th frame, the number of luminance values sf per onepixel is 60.

It is decided whether each of the sixty luminance values sf is smallerthan a threshold value θs. The threshold value θs can be suitablydetermined. The threshold value θs used by the present inventor is 0.15.In other words, a color of a pixel in which a luminance value sf is lessthan 0.15 is regarded as an achromatic color or a substantial achromaticcolor. An initial achromatic color frame set Fm is obtained by the framein which the luminance value sf is smaller than the threshold value θs(STEP722).

A minimum color distance d (Cf) between a color vector Cf of a pixel ina frame f which does not belong to the achromatic color frame set Fm andthe set Fm is calculated (STEP723). The calculation is conducted basedon the following numerical expression.

${d\left( c_{f} \right)} = {\min\limits_{n \in F^{M}}\left( \sqrt{\left( {c_{f} - c_{n}} \right)\left( {c_{f} - c_{n}} \right)^{T}} \right)}$

n when a color distance between the frame f and n is the minimum in theachromatic color frame set Fm is searched based on the numericalexpression.

It is decided whether the obtained d (Cf) is less than a threshold valueθd (STEP724). The threshold value θd can be suitably determined. Thethreshold value θd used by the present inventor is 3.0. In other words,a color of a pixel in which d (Cf) is less than 3.0 is regarded as anachromatic color or a substantial achromatic color. When d (Cf) is lessthan the threshold value θd, the frame is added to the achromatic colorframe set Fm (STEP725). The achromatic color frame set Fm is updated bythe addition. When d (Cf) is equal to or greater than the thresholdvalue θd, the frame is discriminated as the chromatic color frame set(STEP726). The flow is repeated until the discrimination of all theframes as the chromatic color and the achromatic color is completed.

The flow shown in FIG. 17 is conducted for all the pixels except themask 36. For example, when the number of the pixels except the mask 36is 150000, and the number of the frames is 60, luminance values sf of9000000 (150000×60) are calculated.

The calculating part 16 produces a luminance histogram for the wholeframe set (STEP73). An example of the luminance histogram for a certainpixel is shown in FIG. 18. In the luminance histogram, a class isluminance. The histogram includes 100 classes of 1 to 100. In thehistogram, a frequency is a frame number. The frequency may be subjectedto smoothing processing. An example of a luminance histogram of anotherpixel is shown in FIG. 19. An example of a luminance histogram of stillanother pixel is shown in FIG. 20. In each of the luminance histograms,the total of the frames is 98.

The calculating part 16 produces a color histogram for the chromaticcolor frame set and the achromatic color frame set (STEP74). An exampleof the color histogram for a certain pixel is shown in FIG. 21. Thecolor histogram is obtained by combining the histogram of the chromaticcolor frame set with the histogram of the achromatic color frame set. Inthe color histogram, the class of the chromatic color frame set is hue.The class of the hue includes 100 classes of 1 to 100. In the colorhistogram, the class of the achromatic color frame set is luminance. Theclass of the luminance includes 100 classes of 1 to 100. The totalnumber of the classes is 200. In the color histogram, a frequency is aframe number. The frequency may be subjected to smoothing processing. Anexample of a color histogram of another pixel is shown in FIG. 22. Anexample of a color histogram of still another pixel is shown in FIG. 23.In each of the color histograms, the total of the frames is 98.

It is decided whether each of the pixels is the background scene or thephotographic subject based on the luminance histogram and the colorhistogram (STEP75). The decision is conducted by the calculating part16. The decision includes a first stage, a second stage, and a thirdstage. Hereinafter, the stages will be described in detail.

FIG. 24 is a flow chart showing the first stage. The first stage isconducted for each of the pixels. In the first stage, it is first judgedwhether a condition 1 is satisfied (STEP7511). The condition 1 is asfollows.

Condition 1: In the luminance histogram, all the frames are included ina range in which a class width is equal to or less than 20.

Values other than “20” may be used as the class width.

In the luminance histogram of FIG. 18, all the frames are included in arange in which luminance is 12 to 19 (that is, a width is 8). Therefore,the luminance histogram satisfies the condition 1. In the luminancehistogram of FIG. 19, the minimum value of the class is 12, and themaximum value thereof is 72. Therefore, the luminance histogram does notsatisfy the condition 1. In the luminance histogram of FIG. 20, theminimum value of the class is 13 and the maximum value thereof is 77.Therefore, the luminance histogram does not satisfy the condition 1.

Next, it is judged whether a condition 2 is satisfied (STEP7512). Thecondition 2 is as follows.

Condition 2: In the color histogram, all the frames are included in arange in which the class width is equal to or less than 20.

Values other than “20” may be used as the class width.

FIG. 21 is a color histogram for the pixel of FIG. 18. FIG. 22 is acolor histogram for the pixel of FIG. 19. FIG. 23 is a color histogramfor the pixel of FIG. 20. In the color histogram of FIG. 21, all theframes are included in a range in which hue is 59 to 66 (that is, awidth is 7). Therefore, the color histogram satisfies the condition 2.In the color histogram of FIG. 22, the minimum value of the class of hueis 40, and the maximum value thereof is 65. Furthermore, in thehistogram of FIG. 22, the class of luminance has a frequency. Therefore,the color histogram does not satisfy the condition 2. In the colorhistogram of FIG. 23, the minimum value of the class of hue is 16, andthe maximum value thereof is 64. Furthermore, in the histogram of FIG.23, the class of luminance has a frequency. Therefore, the colorhistogram does not satisfy the condition 2.

In the pixels shown in FIGS. 18 and 21, the luminance histogramsatisfies the condition 1, and the color histogram satisfies thecondition 2. When the golf player 24 swings, the golf player 24 moves.Both the golf player 24 and the background scene can be photographed inthe pixel due to the motion. When both the golf player 24 and thebackground scene are photographed, the luminance or the hue of the pixelfluctuates widely. The pixel satisfying both the conditions 1 and 2 is apixel in which the fluctuation of the luminance and the hue is small. Inother words, it is considered that the golf player 24 is notphotographed between the first frame and the final frame in the pixel.The pixel satisfying the conditions 1 and 2 is decided as the“background scene” in all the frames (STEP7513).

The chromatic color and the achromatic color having the same luminancecannot be discriminated in the luminance histogram, but can bediscriminated in the color histogram. The two chromatic colors havingthe same hue and the different luminance cannot be discriminated in thecolor histogram, but can be discriminated in the luminance histogram.When both the conditions 1 and 2 are satisfied in the silhouetteextracting method according to the present invention, the pixel isdecided as the “background scene” in all the frames. In other words, adecision is conducted by considering both the luminance histogram andthe color histogram. Therefore, the pixel which is not the backgroundscene is almost never falsely recognized as the background scene.

Even the pixel in which only the golf player 24 is photographed betweenthe first frame and the final frame can satisfy the conditions 1 and 2.However, as described above, since the golf player 24 is subjected tomasking by the mask 36, the pixel satisfying the conditions 1 and 2 canbe decided as the “background scene” in all the frames.

The pixel in which both the golf player 24 and the background scene arephotographed between the first frame and the final frame does notsatisfy the condition 1 or 2. The decision of the pixel which does notsatisfy the condition 1 or 2 is carried over to a second stage.

Hereinafter, the second stage will be described in detail. In the firststage, the pixel judged as “both the golf player 24 and the backgroundscene are photographed” is further considered in the second stage. FIG.25 is a flow chart showing the second stage. The second stage isconducted for each of the pixels. In the second stage, it is firstjudged whether a condition 3 is satisfied (STEP7521). The condition 3 isas follows.

Condition 3: In the luminance histogram, a range in which the classwidth is equal to or less than 20 includes equal to or greater than 60%of all the frames.

Values other than “20” may be used as the class width. Values other than“60%” may be used as a ratio.

In the luminance histogram of FIG. 19, a range in which luminance is 12to 19 (that is, a width is 8) includes 80 (that is, 81.6%) frames.Therefore, the condition 3 is satisfied. The condition 3 is notsatisfied in the luminance histogram of FIG. 20.

Next, it is judged whether a condition 4 is satisfied (STEP7522). Thecondition 4 is as follows.

Condition 4: In the color histogram, a range in which the class width isequal to or less than 20 includes equal to or greater than 60% of allthe frames.

Values other than “20” may be used as the class width. Values other than“60%” may be used as a ratio.

In the color histogram of FIG. 22, a range in which luminance is 59 to65 (that is, a width is 7) includes 72 (that is, 73.5%) frames.Therefore, the condition 4 is satisfied. The condition 4 is notsatisfied in the color histogram of FIG. 23.

In the pixels shown in FIGS. 19 and 22, the luminance histogramsatisfies the condition 3, and the color histogram satisfies thecondition 4. When the range in which the class width is equal to or lessthan 20 includes equal to or greater than 60% of all the frames, thefluctuation of the luminance or the hue is considered to be small in thepixel of the frame group in the class width. On the other hand, theluminance or the hue of the pixel of the frame group outside the classwidth is considered to be greatly different from the luminance or thehue of the pixel of the frame in the class width. It is considered thatthe background scene is mainly photographed in the pixel and the humanbody of the golf player 24 is temporarily photographed between the firstframe and the final frame from the phenomenon. For the pixel satisfyingthe conditions 3 and 4, the frame in the class width is decided as the“background scene”, and the other frame is decided as the “photographicsubject” (STEP7523).

The chromatic color and the achromatic color having the same luminancecannot be discriminated in the luminance histogram, but can bediscriminated in the color histogram. The two chromatic colors havingthe same hue and the different luminance cannot be discriminated in thecolor histogram, but can be discriminated in the luminance histogram. Adecision is conducted based on both the conditions 3 and 4 in thesilhouette extracting method according to the present invention. Inother words, a decision is conducted by considering both the luminancehistogram and the color histogram. Therefore, false recognition issuppressed.

The decision of the pixel presenting the histogram as shown in FIGS. 20and 23 is carried over to a third stage.

Hereinafter, the third stage will be described in detail. The pixelcarried over in the second stage and the pixel corresponding to the mask36 are further considered in the third stage. Hereinafter, the pixel inwhich a decision of the “background scene” or the “photographic subject”has been already conducted is referred to as a “deciding completionpixel”. On the other hand, the pixel in which the decision of the“background scene” or the “photographic subject” has not yet beenconducted is referred to as a “deciding noncompletion pixel”.

FIG. 26 is a flow chart showing the third stage. In the third stage, adistance image dxy is generated for the deciding noncompletion pixel(STEP7531). The distance image dxy is obtained by adding depth data totwo-dimensional data. Herein, the depth data is a distance to aboundary.

When an initial value of the threshold value θd is 1, it is consideredwhether the deciding completion pixel exists at eight positions near thedeciding noncompletion pixel in which dxy is less than θd (STEP7532).Herein, “eight positions near the deciding noncompletion pixel” implieseight pixels placed at the left position, the upper left position, theupper position, the upper right position, the right position, the lowerright position, the lower position, and the lower left position of thedeciding noncompletion pixel.

When the deciding completion pixel does not exist at eight positionsnear the deciding noncompletion pixel at all, the pixel is decided asthe “photographic subject” in all the frames (STEP7533). When one or twoor more deciding completion pixels exist at eight positions near thedeciding noncompletion pixel, it is judged whether the followingcondition 5 is satisfied (STEP7534). The condition 5 is as follows.

Condition 5: A frame group satisfying the following numericalexpressions exists in the luminance histogram.min(LQ)>min(LB)−θwmax(LQ)<max(LB)+θwIn these numerical expressions, min (LQ) is the minimum value of theclass width of the frame group in the luminance histogram of thedeciding noncompletion pixel; max (LQ) is the maximum value of the classwidth of the frame group in the luminance histogram of the decidingnoncompletion pixel; min (LB) is the minimum value of the class width ofthe frame group which is the background scene in the luminance histogramof one deciding completion pixel existing at eight positions near thedeciding noncompletion pixel; and max (LB) is the maximum value of theclass width of the frame group which is the background scene in theluminance histogram of one deciding completion pixel existing at eightpositions near the deciding noncompletion pixel. θw is suitably set. Thepresent inventor uses 6 as θw.

When one or two or more deciding completion pixels exist at eightpositions near the deciding noncompletion pixel, it is judged whetherthe following condition 6 is further satisfied (STEP7535). The condition6 is as follows.

Condition 6: A frame group satisfying the following numericalexpressions exists in the color histogram.min(CQ)>min(CB)−θwmax(CQ)<max(CB)+θwIn these numerical expressions, min (CQ) is the minimum value of theclass width of the frame group in the color histogram of the decidingnoncompletion pixel; max (CQ) is the maximum value of the class width ofthe frame group in the color histogram of the deciding noncompletionpixel; min (CB) is the minimum value of the class width of the framegroup which is the background scene in the color histogram of onedeciding completion pixel existing at eight positions near the decidingnoncompletion pixel; and max (CB) is the maximum value of the classwidth of the frame group which is the background scene in the colorhistogram of one deciding completion pixel existing at eight positionsnear the deciding noncompletion pixel. θw is suitably set. The presentinventor uses 6 as θw.

The pixel of the frame group satisfying the conditions 5 and 6 isdecided as the “background scene”. The pixel of the frame group whichdoes not satisfy the conditions 5 and 6 is decided as the “photographicsubject” (STEP7536). When either of the conditions 5 and 6 is notsatisfied in the relationship with the deciding completion pixel, andthe other deciding completion pixel exists at eight positions near thedeciding noncompletion pixel, it is judged whether the conditions 5 and6 are satisfied in the relationship with the other deciding completionpixel.

After the consideration of the conditions 5 and 6 is completed for allthe deciding noncompletion pixels, “1” is added to θd (STEP7537). A flowof a consideration (STEP7532) of whether the deciding completion pixelexists at eight positions near the deciding noncompletion pixel of thedeciding noncompletion pixel to a decision (STEP7536) is repeated. Therepetition is conducted until θd reaches to θdmax. θdmax is the maximumvalue in the distance image.

All the pixels of all the frames are discriminated as any one of the“background scene” and the “photographic subject” by the flow. The setof the pixels as the photographic subject is a silhouette of thephotographic subject in each of the frames. A silhouette of a framespecified as an address is shown in FIG. 27. In FIG. 27, the pixel ofthe photographic subject is shown by black, and another pixel is shownby white. As is apparent from FIG. 27, the contour of the photographicsubject is almost faithfully reproduced by the method.

A boundary between the silhouette of FIG. 27 and the background scene isshown in FIG. 28. The boundary is a contour of the photographic subject(golf player 24). FIG. 28 shows the contour of the golf player 24 in theaddress. The calculating part 16 decides the quality of the swing basedon the contour (STEP8).

A flow chart for the deciding the swing from the contour of thephotographic subject is shown in FIG. 29. The calculating part 16determines a feature point from the contour (STEP81). The feature pointis a point expressing the region of the photographic subject such as thegolf player 24 or the golf club 22. The feature point is a pointexpressing the region of the photographic subject used for deciding.Specifically, the calculating part 16 determines a feature point forjudging the posture of the golf player 24 such as a base of a neck, awaist, a knee joint, or an elbow, and a feature point for judging theposition of the golf club such as a grip end of the golf club 22. Thecalculating part 16 acquires position information of the feature point(STEP82). The calculating part 16 decides the quality of the swing fromthe position information (STEP83).

A method for determining a feature point of the base of the neck in theaddress will be described with reference to FIG. 30. The frame of theaddress includes pixels of 480×640. In FIG. 30, the frame is expressedas x-y-coordinates including each pixel as one unit with an upper leftend point P0 (0, 0) as an original point. An upper right end is a pointP1 (479, 0); a lower left end is a point P2 (0, 639), and a lower rightend is a point P3 (479, 639).

A head part search area 38 is provided in FIG. 30. A point P101 of FIG.30 expresses a point on the contour closest from the upper right endpoint P1. When the point on the contour and a distance between the pointP1 and the point are used as a function, the point P101 is an extremevalue, wherein the distance is minimized. Herein, the maximum value orthe minimum value of the function is set to the extreme value. Forexample, both a point on the contour which has a value of x taking amaximum value or a minimum value on the x-y-coordinates and a point onthe contour which has a value of y taking a maximum value or a minimumvalue are extreme values. In a function of a certain point and adistance between the certain point and a line, or a function of acertain point and a distance between the certain point and anotherpoint, the certain point bringing about the maximized or minimizeddistance is an extreme value. A value of an x-coordinate of the pointP101 is expressed as x₁₀₁, and a value of a y-coordinate is expressed asy₁₀₁. Similarly, x of a point P102 is x₁₀₂, and y is y₁₀₂. In thefollowing description, unless otherwise noted, the values of x and y ofeach point are expressed as in the point P101 and the point P102.

The point P102 is an extreme value, wherein x is a maximum value x₁₀₂ inthe pixel constituting the contour in the head part search area 38. Apoint P103 is an extreme value, wherein y is a minimum value y₁₀₃ in thepixel constituting the contour in the head part search area 38. The headpart search area 38 is a predetermined range with the point P101 as abase point. For example, the head part search area is a pixel range inwhich x is x₁₀₁−30 or greater and x₁₀₁+30 or less and y is y_(in)−30 orgreater and y₁₀₁+30 or less. The point P101 to the point P103 specifythe head part of the golf player 24. The predetermined range may bedefined from the geometrical position relation of the region of thephotographic subject. In other words, the search area is set based onthe geometrical position relation between the extreme value and theregion of the human body. The predetermined range of another search areain the following description can be also similarly set.

A back head part search area 40 is provided in FIG. 30. The back headpart search area 40 is a predetermined range with the point P103 as abase point. A point P104 is an extreme value, wherein a value of x is aminimum value x₁₀₄ in the pixel constituting the contour in the backhead part search area 40. The point P104 shows the position of the backhead part. The point P104 is a reference point determined from the pointP101 which is the extreme value. For example, the back head part searcharea 40 is a pixel range in which x is x₁₀₃−10 or greater and x₁₀₃ orless and y is y₁₀₃ or greater and y₁₀₃+10 or less.

A point P105 is an extreme value, wherein a value of x on the contour isa minimum value x₁₀₅. The point P105 shows the position of a back waist.A chain double-dashed line L101 is a straight line passing through thepoint P104 which is the reference point and the point P105 which is theextreme value. A distance between the straight line L101 and the contouris calculated. A distance between the contour located on the left sideof the straight line L101 and the straight line L101 is defined as −(minus). A distance between the contour located on the right side of thestraight line L101 and the straight line L101 is defined as + (plus). Apoint on the contour is defined as a point P106, wherein a distancebetween the point on the contour and the straight line L101 ismaximized. The point P106 shows the feature point of the base of theneck.

A method for determining a feature point of the grip end will bedescribed with reference to FIG. 31. A straight line L102 of FIG. 31shows a shaft line. The shaft line L102 is obtained by subjecting theframe of the top and the frame of the address to difference processing.The shaft line L102 is obtained by Hough transforming after thedifference processing. FIG. 31 is obtained by stacking the shaft lineL102 on the silhouette of FIG. 27.

A grip end search area 42 is provided in FIG. 31. For example, a firstframe 26 is used for the grip end search area 42. A point P109 and apoint P110 show intersection points between the contour and the shaftline L102. In the grip end search area 42, the intersection point havinga small value of x is defined as the grip end. In FIG. 31, x₁₀₉ issmaller than x₁₁₀. The point P109 is defined as the grip end.

The number of the intersection points between the contour and the shaftline L102 may be 1 in the determination of the feature point of the gripend. For example, it is a case of a silhouette in which a hand and anabdomen are integrated with each other. In this case, the one point isdetected. When two points are not detected, it is decided that the gripend is not detected. The swing can be diagnosed with the one pointexcluded from diagnosing processing.

A method for determining feature points of a right knee point and aright knee joint will be described with reference to FIG. 32. A heelsearch area 44 is provided in FIG. 32. The heel search area 44 is apredetermined range with the point P105 as a base point. For example,the heel search area 44 is a pixel range in which x is x₁₀₅ or greaterand 479 or less and y is y₁₀₅ or greater and 639 or less. A point P111is a point on the contour in the heel search area 44. The point P111 isa point closest to the point P2, and is an extreme value. The point P111shows a heel.

A chain double-dashed line L103 is a straight line passing through thepoint P105 which is an extreme value, and the point P111 which is anextreme value. In a function of a point on the contour of the point P105to the point P111 and a distance between the point on the contour andthe straight line L103, a point P112 is an extreme value, wherein thedistance is maximized. The point P112 shows a temporary posterior knee.A feature point P113 of a posterior knee is determined using a Beziercurve from the point P112 of the temporary posterior knee.

As shown in FIG. 33, a control point is set in a predetermined range onthe basis of the point P112 of the temporary posterior knee. Herein, asan example, the contour is approximated with the Bezier curve at sevencontrol points Pc1 to Pc7 disposed at equal intervals. The controlpoints Pc1 to Pc7 are points on the contour. An upper end point of thepredetermined range is the point Pct, and a lower end point thereof isthe point Pc7. The contour is equally divided into four between thepoint P105 and the point P112. Middle points are defined as the pointPct, the point Pc2, and the point Pc3 toward the point P112 side fromthe point P105 side by equally dividing the contour into four. The pointP112 is defined as the point Pc4. The contour between the point P112 andthe point P111 is equally divided into four. Middle points are definedas the point Pc5, the point Pc6, and the point Pc7 toward the point P111side from the point Pc4 side by equally dividing the contour into four.

For example, the approximation of the Bezier curve is conducted by usinga total evaluation value VAL. Specifically, when an evaluation value ofa portion in which a value of y is smaller than the control point Pc4 isdefined as val(a) in the range of the point Pc1 to the point Pc7 shownin FIG. 33, and an evaluation value of a portion in which a value of yis greater than the control point Pc4 is defined as val(b), the totalevaluation value VAL is calculated by the following numericalexpression.VAL=(val(a)+val(b))−ABS(val(a)−val(b))

The evaluation value val(a) is a total value of a difference between theBezier curve and the contour in the range of the point Poi to the pointPc4. The evaluation value val(b) is a total value of a differencebetween the Bezier curve and the contour in the range of the point Pc4to the point Pc7. Herein, ABS (val(a)−val(b)) is an absolute value of adifference between the evaluation value val(a) and the evaluation valueval(b).

A plurality of examples of the point Pc1 to the point Pc7 are set withthe point Pc4 fixed between the point P105 and the point P111. TheBezier curve when the total evaluation value VAL is maximized in theplurality of examples is determined. When the total evaluation value VALis maximized, the Bezier curve is most approximated to the contour.

A temporary feature point P112′ is provided in a predetermined range onthe basis of the point P112 of the temporary posterior knee. Forexample, the predetermined range is a pixel range in which a valuex_(112′) of x is constant and a value of y is y₁₂₂−20 to y₁₁₂+30. Thevalue x_(112′) is greater than the value x₁₁₂. Thereby, the point P112′is located on a + (plus) side in an x-axis direction of the contour ofthe point Pc1 to the point Pc7. The value of y of the point P112′ isincreased and decreased. The point P112′ moves up and down, and thecontour is approximated again with the Bezier curve with the point P112′as the control point. When the total evaluation value VAL is maximizedas in the approximation in the Bezier curve, the Bezier curve is mostapproximated to the contour. The point on the contour closest from thepoint P112′ which is the control point when the Bezier curve is mostapproximated is defined as a feature point P113 of the posterior knee.

A chain double-dashed line L104 of FIG. 32 is a straight line passingthrough the point P113. The chain double-dashed line L104 is a straightline orthogonal to the straight line L103. A point P114 is anintersection point between the straight line L104 and the contour. Thepoint P114 is defined as a temporary knee point. The contour isapproximated with the Bezier curve on the basis of the point P114. As inthe feature point P113, the Bezier curve is approximated to the contourto determine the feature point. Thus, a point P115 is determined as thefeature point of the knee point.

A point P116 of FIG. 32 shows a middle point of the point P113 and thepoint P115. The point P116 is defined as the feature point of the kneejoint. The point P116 which is the feature point of the knee joint isdetermined based on the geometrical position relation of the region ofthe human body.

A method for determining a feature point of a right tiptoe and abackbone line will be described with reference to FIG. 34. A chaindouble-dashed line 105 of FIG. 34 is a straight line passing through thepoint P111. The straight line is parallel to an x-axis. A point P117 isan intersection point between the straight line L105 and the contour.The point P117 is the feature point of the right tiptoe.

A front waist search area 46 of FIG. 34 is a predetermined range withthe point P105 as a base point. The front waist search area 46 is apixel range in which x is x₁₀₅ or greater and x₁₁₇ or less and y is y₁₀₅or greater and y₁₁₄ or less. A point P118 is a point on the contour. Thepoint P118 is an extreme value, wherein a distance between the pointP118 and the point P105 is minimized. The point P118 is the featurepoint of the front waist. A chain double-dashed line L106 is a straightline passing through the point P105 and the point P118. A point P119 isa point on the straight line L106. When a distance between the pointP105 and the point P118 is defined as D1, the point P119 is located at adistance of ⅓ times of D1 from the point P105. The point P119 shows thefeature point of the waist. The point P119 is determined based on thegeometrical position relation of the region of the human body. A chaindouble-dashed line L107 is a straight line passing through the pointP106 of the base of the neck and the point P119. The straight line L107is the backbone line in the address.

A method for determining a right thigh line will be described withreference to FIG. 34. A chain double-dashed line L108 of FIG. 34 is astraight line passing through the point P119 and the point P116. Thestraight line L108 is the right thigh line.

A method for determining feature points of a thenar and a right anklewill be described with reference to FIG. 35. A point P120 of FIG. 35 isa point on the straight line L105. When a distance between the pointP111 and the point P117 is defined as D2, the point P120 is a pointlocated at a distance of 6/7 times of D2 from the point P111. The pointP120 shows the feature point of the thenar. The point P120 is determinedbased on the geometrical position relation of the region of the humanbody.

A point P121 of FIG. 35 is a point on the straight line L105. The pointP121 is a point located at a distance of ⅜ times of D2 from the pointP111. The point P121 shows the feature point of the right ankle. Thepoint P121 is determined based on the geometrical position relation ofthe region of the human body.

A method for determining a feature point of a right shoulder will bedescribed with reference to FIG. 36. A straight line L109 of FIG. 36 isa straight line passing through the point P106 and the point P109. Apoint P122 is a point on the straight line L109. When a distance betweenthe point P106 and the point P109 is defined as D3, the point P122 is apoint located at a distance of ⅜ times of D3 from the point P106. Achain double-dashed line L110 is a straight line passing through thepoint P122 and extending in the direction of the x-axis. A point P123 isan intersection point between the straight line L101 and the straightline L110. A point P124 is a point on the straight line L110. The pointP124 is located between the point P122 and the point P123. When adistance between the point P123 and the point P122 is defined as D4, thepoint P124 is a point located at a distance of ¾ times of D4 from thepoint P123. The point P124 shows the feature point of the rightshoulder. The point P124 is determined based on the geometrical positionrelation of the region of the human body.

Thus, the feature points are determined from the contour of the golfplayer 24. The calculating part 16 determines the feature point P106 ofthe base of the neck of the golf player, the feature point P116 of theright knee joint, and the feature point P119 of the waist, or the like.The calculating part 16 determines the backbone line (straight lineL107), and the thigh line (straight line L108) or the like from thesefeature points.

A method for determining a feature point from the contour of the golfplayer 24 of a predetermined position during a takeback will bedescribed with reference to FIGS. 37 to 39. In FIG. 37, a dominant armof the golf player 24 is in a horizontal state. The contour is obtainedby silhouette extraction as in the contour of the address.

A method for determining a feature point of the base of the neck will bedescribed with reference to FIG. 37. A point P201, a point P202, a pointP203, a point P204, a point P205, and a straight line L201 aredetermined as in the point P101, the point P102, the point P103, thepoint P104, the point P105, and the straight line L101, and thedescriptions thereof are omitted herein.

A point P206 is a center of gravity of a silhouette S1 portion locatedon the left side of the straight line 201. A straight line L202 is astraight line extending in a direction of a y-axis from the point P206.A point P207 is an intersection point between the straight line L202 andthe contour, and is a reference point. The point P207 has a value of ysmaller than that of the point P206. A straight line L203 is a straightline passing through the point P204 and the point P207. A point P208 isa point located on the contour between the point P204 and the pointP207, wherein a distance between the straight line L203 and the pointP208 is maximized. The point P208 shows the feature point of the base ofthe neck. In the takeback, it is hard to specify the feature point ofthe base of the neck as in the point P106 of the address. Herein, thepoint P208 is easily determined by using the point P207 as the referencepoint. The posture of the photographic subject is different for everycheck frame. Thus, the extreme value, the reference point, and thefeature point which are suitable for the different posture aredetermined. Thereby, the quality of the swing can be easily andaccurately decided.

A method for determining the backbone line will be described withreference to FIG. 38. A point P209 is the feature point of the frontwaist. The point P209 is determined as in the feature point P118 of thefront waist, and the description thereof is omitted herein. A straightline L204 is a straight line passing through the point P205 and thepoint P209. A point P210 is a point on the straight line L204. When adistance between the point P205 and the point P209 is defined as D5, thepoint P210 is a point located at a distance of ½ times of D5 from thepoint P205. The point P210 shows the feature point of the waist. Thepoint P210 is determined based on the geometrical position relation ofthe region of the human body. A chain double-dashed line 205 is astraight line passing through the point P208 and the point P210. Thestraight line L205 is the backbone line.

Although the point P205 of FIG. 38 is determined as in the point P105,another feature point may be determined in place of the point P205. Forexample, a plurality of points on the contour including the point P205are determined as the reference point. The contour is approximated to apolynomial expression with a least squares method. A point on anapproximate line approximated to the polynomial expression and havingminimum x may be the feature point. The point and the straight linescorresponding to the point P210, the straight line L204, and thestraight line L205 may be specified by using the feature point in placeof the point P205 which is the extreme value.

A method for determining a feature point of a left tiptoe will bedescribed with reference to FIG. 39. A feature point P211 of the righttiptoe is determined as in the feature point P117 of the right tiptoe.The contour of the right tiptoe including the point P211 is prepared asthe template. The template including the point P211 is matched with thecontour. A portion of the contour with which the template is mostmatched is defined as the left tiptoe. When the matching is conducted, apoint P212 is a point of a position corresponding to the point P211. Thepoint P212 shows the feature point of the left tiptoe. Herein, thetemplate is matched with the left tiptoe with the contour of the righttiptoe having a relative position specified from the feature point P211as the template. A point of a position corresponding to the featurepoint P211 specified from the template of the right tiptoe is specifiedas the point P212. The feature point P117 of the right tiptoe of theaddress and the contour including the point P117 may be used as thetemplate.

A straight line L206 of FIG. 39 is a straight line passing through thepoint P212. The straight line L206 extends in the direction of thex-axis. A straight line L207 shows a shaft line. The shaft line L207 isobtained by subjecting a frame of a predetermined position during atakeback and a frame of an address to difference processing. The shaftline L207 is obtained by Hough transforming after difference processing.FIG. 39 is obtained by stacking the shaft line L207 on a silhouette ofFIG. 37. A chain double-dashed line L208 is an extension line of theshaft line L207. A point P213 is an intersection point between thestraight line L206 and the straight line 208.

A point P214 of FIG. 39 shows a feature point of a center position of aball before hitting. The silhouette of the ball before hitting isobtained by subjecting the frame before the impact and the frame afterthe impact to difference processing. The point P214 is obtained as themiddle point of the direction of the x-axis and the direction of they-axis in the silhouette of the ball, for example.

A method for determining a feature point from the contour of the golfplayer 24 of the top will be described with reference to FIGS. 40 to 42.The contour is obtained by the silhouette extraction as in the contourof the address.

A method for determining a feature point of a wrist will be describedwith reference to FIG. 40. A point P301, a point P302, a point P303, apoint P304, a point P305, a straight line L301, and a straight line L302are respectively determined as in the point P204, the point P208, thepoint P205, the point P209, the point P210, the straight line L204, andthe straight line L205, and the descriptions thereof are omitted herein.The contour of the head part including the point P204 and the point P208may be prepared as the template, and the template may be matched. Thecontour of FIG. 40 may be matched with the template, and pointscorresponding to the point P204 and the point P208 may be respectivelydetermined as the point P301 and the point P302.

A wrist search area 48 of FIG. 40 is a predetermined range with thepoint P301 as a base point. The wrist search area 48 is a pixel range inwhich x is 0 or greater and x₃₀₁ or less and y is 0 or greater and y₃₀₅or less. y of a point P306 is a minimum value in the wrist search area48. The point P306 is a feature point of a hand. A point P307 is a pointmoved by a predetermined amount in the direction of the y-axis from thepoint P306. For example, a value y₃₀₇ of the y-coordinate of the pointP307 is y₃₀₆+10. The point P307 is the feature point of the wrist. Thepoint P307 is determined based on the geometrical position relation ofthe region of the human body.

A method for determining a feature point of a right elbow and a rightarm line will be described with reference to FIG. 41. A point P308 is apoint moved by a predetermined amount in the direction of the y-axisfrom the point P305. For example, a value y₃₀₈ of the y-coordinate ofthe point P308 is y₃₀₅−50. A right elbow search area 50 is apredetermined range with the point P301 as a base point. The right elbowsearch area 50 is a pixel range in which x is 0 or greater and x₃₀₁ orless and y is 0 or greater and y₃₀₈ or less. x of a point P310 is aminimum value in the right elbow search area 50. A point P311 is a pointmoved by a predetermined amount in the direction of the x-axis from thepoint P310. For example, a value x₃₁₁ of the x-coordinate of the pointP311 is x₃₁₀+10. The point P311 is the feature point of the right elbow.The point P311 is determined based on the geometrical position relationof the region of the human body. The chain double-dashed line L302 is astraight line passing through the point P307 and the point P311. Thestraight line L302 is the right arm line.

A method for determining feature points of right and left knee pointswill be described with reference to FIG. 42. A point P312 is the featurepoint of a right posterior knee. The point P312 is determined as in thepoint P113 of the address. A chain double-dashed line L303 is a straightline extending in the direction of the x-axis from the point P312. Apoint P313 is an intersection point between the straight line L303 andthe contour. x of a point P314 is a maximum value around the point P313.The point P314 is a temporary left knee point. The contour isapproximated with the Bezier curve on the basis of the point P314. As inthe feature point P113, the Bezier curve is approximated to the contour,and the feature point is determined. Thus, a point P315 is determined asthe feature point of the left knee point.

Although not shown in the drawings, the contour of an area of the waistto the right posterior knee is linearly approximated by using the edgeimage of the frame of the top. For example, the contour is linearlyapproximated by using the least squares method. A chain double-dashedline L304 of FIG. 42 shows the approximated straight line. A kneecontour search area 52 is a predetermined range with the point P303 as abase point. For example, the knee contour search area 52 is a pixelrange in which x is x₃₀₄−30 or greater and x₃₀₄ or less and y is y₃₀₃+35or greater and y₃₁₂ or less. In the edge image, a straight line almostparallel to the straight line L304 is searched in the knee contoursearch area 52. A straight line L305 shows a straight line parallel tothe straight line L304 determined in the edge image. A point P316 is anintersection point between the straight line L305 and the straight lineL303. The point P316 shows the feature point of the right knee point.

A width of a foot may be previously measured in place of determining thestraight line L305. A parallel line may be drawn with respect to thestraight line L304 with the clearance of the width of the foot on theknee point side. The intersection point between the parallel line andthe straight line L303 may be the feature point of the right knee point.

The contour of the golf player 24 of the impact obtained from the checkframe is shown in FIGS. 43 and 44. A method for determining a featurepoint from the contour of the golf player 24 will be described withreference to FIGS. 43 and 44. The contour is obtained by the silhouetteextraction.

A point P401, a point P402, a point P403, a point P404, a straight lineL401, and a straight line L402 of FIG. 43 are respectively determined asin the point P208, the point P205, the point P209, the point P210, thestraight line L204, and the straight line L205, and the descriptionsthereof are omitted herein. A point P405 is a point on the straight lineL401. When a distance between the point P404 and the point P403 isdefined as D6, the point P405 is a point located at a distance of ½times of D6 from the point P404. The point P405 is determined from thegeometrical position relation of the region of the human body. In thedetermination of the point P401, the contour of the head part includingthe point P208 may be prepared as the template, and the template may bematched. The contour of FIG. 40 may be matched with the template, todetermine a point corresponding to the point P208 as the point P401.

A right knee point search area 54 of FIG. 43 is a predetermined rangewith the feature point P403 of a front waist as a base point. Forexample, the predetermined range is a pixel range in which x is x₄₀₃−30or greater and x₄₀₄+50 or less and y is y₄₀₃ or greater and y₂₁₂ orless. A point P406 having x as a maximum value in the right knee pointsearch area 54 is defined as a temporary knee point. The contour isapproximated with the Bezier curve on the basis of the point P406. As inthe feature point P113, the Bezier curve is approximated to the contour,and the feature point is determined. Thus, a point P407 is determined asthe feature point of the right knee point.

Although not shown in the drawings, an edge is searched in a minusdirection of the x-axis from the point P407 using the edge image of theframe of the impact. When the edge is in the area of the silhouette ofthe golf player 24, the position of the edge is defined as a point P408.The point P408 shows the feature point of the right posterior knee.

In the method for determining the point P408, the width of the foot maybe previously measured. The feature point P408 of the right posteriorknee may be determined with the clearance of the width of the foot fromthe point P407. The method may be used when the edge is not discoveredin the area of the silhouette of the golf player 24.

A chain double-dashed line L403 is a straight line passing through thepoint P407 and the point P408. A point P409 is located on the straightline L403, and is located between the point P407 and the point P408.When a distance between the point P408 and the point P407 is defined asD7, the point P409 is a point located at a distance of ½ times of D7from the point P408. The point P409 is the feature point of the rightknee joint.

A chain double-dashed line L404 is a straight line passing through thepoint P405 and the point P409. The straight line L404 shows the rightthigh line.

A method for determining a feature point of the right ankle and a lowerthigh line will be described with reference to FIG. 44. The point P111is the feature point of the heel determined in the address. A point P410is the feature point of the tiptoe. The point P410 uses the featurepoint P117 of the tiptoe of the address. The point P410 may bedetermined from the contour of the impact. A chain double-dashed lineL405 is a straight line passing through the point P111 and the pointP410. A point P411 is a point on the straight line L405. The point P411is located between the point P111 and the point P410. When a distancebetween the point P111 and the point P410 is defined as D8, the pointP411 is a point located at a distance of ½ times of D8 from the pointP111.

A heel search area 56 is a predetermined range with the point P402 as abase point. The predetermined range is a pixel range in which x is x₄₀₂or greater and x₄₁₁ or less and y is y₄₀₂ or greater and y₁₁₁−10 orless, for example. A point P412 is a point on the contour, wherein adistance between a point P5 located at the lower left corner of the heelsearch area 56 and the point 412 is minimized in the heel search area56. The point P412 shows the feature point of the heel. A chaindouble-dashed line L406 is a straight line passing through the pointP412 and the point P410. A point P413 is a point on the straight lineL406. The point P413 is located between the point P412 and the pointP410. When a distance between the point P412 and the point P410 isdefined as D9, the point P413 is a point located at a distance of 3/10times of D9 from the point P412. The point P413 shows the feature pointof the right ankle. A chain double-dashed line L407 is a straight linepassing through the point P413 and the point P409. The straight lineL407 shows the lower thigh line.

The calculating part 16 decides the quality of the posture of the golfplayer 24 based on these feature points and the lines determined fromthe feature points. The posture of the golf player and the quality ofthe swing are decided based on the positions of the feature points ofthe plurality of different check frames and the positions of the linesdetermined from the feature points (STEP83).

A method for deciding the quality of the address will be described as anexample with reference to FIG. 45. A chain double-dashed line L502 ofFIG. 45 is a balance point line. A double-pointed arrow al is aninclination of a backbone line L107 with respect to the horizontaldirection. The balance point line L502 is a straight line passingthrough the feature point P120 of the thenar and extending in a verticaldirection.

For example, the calculating part 16 acquires the position informationof the feature point. The following judging indices E1 to E4 arecalculated in the address from the position information.E1=x ₁₂₄ −x ₁₂₀E2=x ₁₁₅ −x ₁₂₀E3=α1E4=x ₁₀₉ −x ₁₁₈

When the judging index E1 is in a predetermined range, the calculatingpart 16 judges that the position of the right shoulder is close to thebalance point line L502. The calculating part 16 judges that theposition of the right shoulder is excellent. When the judging index E1is out of the predetermined range, the calculating part 16 judges thatthe balance is poor. In the address in which the balance is poor, shotis apt to be unstable. The predetermined range is −10 or greater and +10or less, for example.

Similarly, when the judging index E2 is in a predetermined range, thecalculating part 16 judges that the position of the knee is close to thebalance point line L502. The calculating part 16 judges that theposition of the knee is excellent. When the judging index E2 is out ofthe predetermined range, the calculating part 16 judges that the balanceis poor. In the address in which the balance is poor, shot is apt to beunstable. The predetermined range is −10 or greater and +10 or less, forexample.

The judging index E3 is a spine angle. When the judging index E3 is in apredetermined range, the calculating part 16 judges that the spine angleis excellent. When the judging index E3 is smaller than thepredetermined range, the golf player 24 is hard to use the power of thelower body. The loss of a flight distance is increased. When the judgingindex E3 is larger than the predetermined range, a rotation axis isunstable. Shot is apt to be unstable. The predetermined range of thejudging index E3 is 50 degrees or greater and 70 degrees or less, forexample.

When the judging index E4 is in a predetermined range, the calculatingpart 16 judges the position of the grip is excellent. When the judgingindex E4 is smaller than the predetermined range, the golf player 24 ishard to swing arms. The loss of the flight distance is increased. Whenthe judging index E4 is greater than the predetermined range, the golfplayer 24 is hard to maintain the movement balance of the body and thearms. The predetermined range is 5 or greater and 20 or less, forexample.

Herein, the address is described as an example. However, each posturecan be judged also in the check frames of the predetermined positionduring the takeback, the top, the quick turn, the impact, and thefinish. The quality of the swing is evaluated by comparing the judgingindices of the check frames. For example, it can be judged whether therotation axis is stable by comparing a spine angle α1 of the addresswith the spine angle at the predetermined position during the takeback.The calculating part 16 decides the quality of the swing in each checkframe based on the predetermined judging indices. The calculating part16 compares the judging indices obtained from the two or more differentcheck frames to decide the quality of the swing. When the judgement ofall the judging indices is completed, the decision of the quality of theswing is completed (STEP8).

In the diagnosing method, a plurality of check frames are determinedfrom the image data (STEP6). The swing diagnosis in various postures isconducted. The quality of the change of the posture is diagnosed betweenthe different postures. The diagnosing method can be used for thesynthetic diagnosis of the swing.

In the diagnosing method, a point on the contour which is the extremevalue is determined. The base of the neck, the knee joint, the backboneline, the thigh line, the ankle, and the waist or the like aredetermined as the feature point from the extreme value. The extremevalue on the contour is easily determined, and incorrect determinationcan be suppressed. Since the feature point is determined based on theextreme value, the feature point is easily determined, and the incorrectdetermination is suppressed. The judging method enables accuratedecision. The judging method can shorten a processing time for thedecision.

Furthermore, these feature points, the shaft line of the golf club, andthe ball position are specified, and thereby the quality of the swing ofthe golf player can be accurately decided.

In the embodiment, the contour is determined from the binary image bythe silhouette extraction. However, the contour may be determined byanother method. For example, the contour may be determined by subjectingthe plurality of frames to difference processing. The feature point maybe determined for the contour as described above.

A diagnosing method according to another embodiment of the presentinvention will be described with reference to FIG. 46. In theembodiment, a difference image obtained by difference processing isused. A difference image obtained by subjecting an address and a top todifference processing is shown in FIG. 46. The difference image issubjected to camera shake correction.

The difference image is first subjected to contraction processing toremove dot noise or the like. Preferably, the contraction processing isconducted a plurality of times. For example, the contraction processingis conducted three times. Next, labeling processing is conducted. In thelabeling processing, a region having an area having a predeterminednumber or greater of pixels is left, and a region having an area havinga predetermined number or less of pixels is removed. For example, thepredetermined number of pixels in the labeling processing is 150. Next,expansion processing is conducted. The size of the image is returned toa state before the contraction processing by the expansion processing.Preferably, the expansion processing is conducted a plurality of times.For example, the expansion processing is conducted four times.

Herein, the contraction processing, the labeling processing, and theexpansion processing are conducted after the difference processing.Thereby, the contour of the golf player 24 can be more accurately andeasily identified. The feature point can be easily determined based onthe contour.

An example of a method for determining a feature point in the addresswill be described with reference to FIG. 46. The image includes pixelsof 480×640. In FIG. 46, a frame is expressed as x-y-coordinatesincluding each pixel as one unit with an upper left point P0 (0, 0) asan original point. An upper right end is a point P1 (479, 0); a lowerleft end is a point P2 (0, 639), and a lower right end is a point P3(479, 639).

Although not shown in the drawings, a head part search area and a backhead part search area are provided as in the embodiment. A point P601, apoint P602, a point P603, a point P604, a point 2605, and a straightline L601 of FIG. 46 are determined as in the point P101, the pointP102, the point P103, the point P104, the point P105, and the straightline L101 of FIG. 30. Herein, the descriptions thereof are omitted.

Herein, there is shown the method for subjecting the address and the topto the difference processing to determine the extreme value of the headpart of the address and the extreme value of the posterior waist. Thecombination of the frames subjected to the difference processing isreplaced in the plurality of frames, and another extreme value isdetermined by using the difference image. The feature point can bedetermined from the extreme value. The feature point in another posturecan be determined.

Although the calculating part 16 of the server 6 conducts each ofprocessings in the embodiment, the calculating part 16 of the mobiletelephone 4 may conduct each of the processings. In the case, theconnection of the mobile telephone 4 and the server 6 is unnecessary.

The method according to the present invention can diagnose the swingperformed in a golf course, a practice range, a golf shop, and a gardenof a general household or the like.

What is claimed is:
 1. A diagnosing method of a golf swing comprisingthe steps of: a camera photographing a golf player swinging a golf clubto hit a golf ball and the golf club to obtain image data; determining,by a processor, a check frame in which the golf player is in apredetermined posture from a plurality of frames obtained from the imagedata; determining, by the processor, a contour of the golf player fromthe check frame; deciding, by the processor, the swing from the contourof the golf player, and determining extreme values constituting thecontour, the extreme values being maximum values or minimum values of apredetermined function; determining at least one reference point fromthe extreme values; determining a plurality of feature points from theextreme values, each of said plurality of feature points being a pointon the contour and expressing a region of the photographed golf player,wherein each of the plurality of feature points is determined such thata distance between the point on the contour and a straight line thatpasses the reference point and another point on the contour with extremevalues, which is not designated as the at least one reference point, ismaximized or minimized; calculating judging indices from a differencebetween coordinates of two feature points; and diagnosing the swingusing position information of the plurality of feature points, whereinquality of swing is diagnosed based on whether the judging indices arein a predetermined range or not.
 2. The diagnosing method according toclaim 1, wherein the extreme values constitute a contour of a head part,a contour of a waist, or a contour of a heel.
 3. The diagnosing methodaccording to claim 1, further comprising the steps of: determining apoint on the contour to be a reference point based on the extreme value;subjecting the contour including the reference point to polynomialapproximation to obtain an approximate line; and determining a point onthe approximate line, by the same determination as the extreme valuesthat is maximum value or a minimum value of a predetermined function, tobe another feature point.
 4. The diagnosing method according to claim 1,further comprising the steps of: defining a part of the contour of whicha relative position from the feature point is specified as a template;matching the template with another region of the contour; and when thetemplate is most approximated to another region of the contour, defininga point of a position corresponding to the feature point specified fromthe template as another feature point.
 5. The diagnosing methodaccording to claim 1, further comprising the step of determining a pointon a straight line extended from the feature point and having a maximumedge to be another feature point.
 6. The diagnosing method according toclaim 1, wherein a point determined based on a geometrical positionrelation of a region of a human body from the extreme values, areference point obtained from the extreme values, or the feature pointis defined as another feature point.
 7. The diagnosing method accordingto claim 6, wherein the geometrical position relation of the region ofthe human body is a position relation in the check frame in which thegolf player is in the predetermined posture.
 8. The diagnosing methodaccording to claim 1, further comprising the steps of: setting apredetermined search area on the basis of the extreme values, areference point obtained from the extreme value, or the feature point;and defining a point from the extreme values which are within the searcharea as another feature point.
 9. The diagnosing method according toclaim 8, wherein the predetermined search area is set based on ageometrical position relation between the extreme values, the referencepoint, or the feature point and the region of the human body.
 10. Thediagnosing method according to claim 9, wherein the geometrical positionrelation of the region of the human body is a position relation in thecheck frame in which the golf player is in the predetermined posture.11. The diagnosing method according to claim 1, wherein a binary imageof a silhouette of the golf player is obtained from the check frame inthe step of determining the contour of the golf player from the checkframe; and the contour of the golf player is determined from the binaryimage.
 12. The diagnosing method according to claim 1, wherein adifference image is obtained by subjecting the plurality of frames todifference processing in the step of determining the contour of the golfplayer from the check frame; and the contour of the golf player isdetermined from the difference image.
 13. The diagnosing methodaccording to claim 1, further comprising the step of conducting camerashake correction, wherein the plurality of frames obtained from theimage data are subjected to the camera shake correction.
 14. Thediagnosing method according to claim 13, wherein the image data issubjected to the camera shake correction in the step of conducting thecamera shake correction.
 15. A diagnosing system of a golf swingcomprising: (A) a camera photographing a golf player swinging a golfclub to hit a golf ball and the golf club; (B) a memory storingphotographed image data; and (C) a calculating part, wherein thecalculating part comprising: (C1) a function for extract a plurality offrames from the image data; (C2) a function for determining a checkframe in which the golf player is in a predetermined posture from theplurality of frames; (C3) a function for determining a contour of thegolf player of the check frame; (C4) a function for determining extremevalues from the contour, the extreme values being maximum values orminimum values of a predetermined function; (C5) a function ofdetermining at least one reference point from the extreme values; (C6) afunction for determining a plurality of feature points from the extremevalues, each of said plurality of feature points being a point on thecontour and expressing a region of the photographed golf player, whereineach of the plurality of feature points is determined such that adistance between the point on the contour and a straight line thatpasses the reference point and another point on the contour with extremevalues, which is not designated as the at least one reference point, ismaximized or minimized; (C7) calculating judging indices from adifference between coordinates of two feature points; and (C8) afunction for diagnosing the swing using position information of theplurality of feature points, wherein quality of swing is diagnosed basedon whether the judging indices are in a predetermined range or not. 16.The diagnosing system of the golf swing according to claim 15, whereinthe calculating part has a function for subjecting the image data tocamera shake correction.